Fit log returns to F-S skew standardized Student-t distribution.
m is the location parameter.
s is the scale parameter.
nu is the estimated shape parameter (degrees of freedom).
xi is the estimated skewness parameter.

Log returns data 2011-2023.

For 2011, medium risk data is used in the high risk data set, as no high risk fund data is available prior to 2012.
vmrl is a long version of Velliv medium risk data, from 2007 to 2023. For 2007 to 2011 (both included) no high risk data is available.

Summary of gross returns

##       vmr             pmr             mmr             vhr       
##  Min.   :0.868   Min.   :0.904   Min.   :0.988   Min.   :0.849  
##  1st Qu.:1.044   1st Qu.:1.042   1st Qu.:1.013   1st Qu.:1.039  
##  Median :1.097   Median :1.084   Median :1.085   Median :1.099  
##  Mean   :1.070   Mean   :1.065   Mean   :1.066   Mean   :1.085  
##  3rd Qu.:1.136   3rd Qu.:1.107   3rd Qu.:1.101   3rd Qu.:1.160  
##  Max.   :1.168   Max.   :1.141   Max.   :1.133   Max.   :1.214  
##       phr             mhr       
##  Min.   :0.878   Min.   :0.977  
##  1st Qu.:1.068   1st Qu.:1.013  
##  Median :1.128   Median :1.113  
##  Mean   :1.095   Mean   :1.087  
##  3rd Qu.:1.182   3rd Qu.:1.128  
##  Max.   :1.208   Max.   :1.207
##       vmrl      
##  Min.   :0.801  
##  1st Qu.:1.013  
##  Median :1.085  
##  Mean   :1.061  
##  3rd Qu.:1.128  
##  Max.   :1.193
##            vmr   pmr   mmr   vhr   phr   mhr
## Min.   : 0.868 0.904 0.988 0.849 0.878 0.977
## 1st Qu.: 1.044 1.042 1.013 1.039 1.068 1.013
## Median : 1.097 1.084 1.085 1.099 1.128 1.113
## Mean   : 1.070 1.065 1.066 1.085 1.095 1.087
## 3rd Qu.: 1.136 1.107 1.101 1.160 1.182 1.128
## Max.   : 1.168 1.141 1.133 1.214 1.208 1.207

Ranking

Min. : ranking 1st Qu.: ranking Median : ranking Mean : ranking 3rd Qu.: ranking Max. : ranking
0.988 mmr 1.068 phr 1.128 phr 1.095 phr 1.136 vmr 1.168 vmr
0.977 mhr 1.044 vmr 1.113 mhr 1.087 mhr 1.107 pmr 1.141 pmr
0.904 pmr 1.042 pmr 1.099 vhr 1.085 vhr 1.101 mmr 1.133 mmr
0.878 phr 1.039 vhr 1.097 vmr 1.070 vmr 1.160 vhr 1.214 vhr
0.868 vmr 1.013 mmr 1.085 mmr 1.066 mmr 1.182 phr 1.208 phr
0.849 vhr 1.013 mhr 1.084 pmr 1.065 pmr 1.128 mhr 1.207 mhr

Covariance

## cov(vmr, pmr) =  -0.001094875
## cov(vhr, phr) =  -0.0001730651

Velliv medium risk, 2011 - 2023

## 
## AIC: -27.8497 
## BIC: -25.58991 
## m: 0.0480931 
## s: 0.1198426 
## nu (df): 3.303595 
## xi: 0.03361192 
## R^2: 0.993 
## 
## An R^2 of 0.993 suggests that the fit is extremely good.
## 
## What is the risk of losing max 10 %? =< 7.4 percent
## What is the risk of losing max 25 %? =< 1.8 percent
## What is the risk of losing max 50 %? =< 0.2 percent
## What is the risk of losing max 90 %? =< 0 percent
## What is the risk of losing max 99 %? =< 0 percent
## 
## What is the chance of gaining min 10 %? >= 41 percent
## What is the chance of gaining min 25 %? >= 0 percent
## What is the chance of gaining min 50 %? >= 0 percent
## What is the chance of gaining min 90 %? >= 0 percent
## What is the chance of gaining min 99 %? >= 0 percent

QQ Plot

The qq plot looks great. Log returns for Velliv medium risk seems to be consistent with a skewed t-distribution.

Data vs fit

Let’s plot the fit and the observed returns together.

Estimated distribution

Now lets look at the CDF of the estimated distribution for each 0.1% increment between 0.5% and 99.5% for the estimated distribution:

We see that for a few observations out of a 1000, the losses are disastrous, while the upside is very dampened.

Monte Carlo

## Down-and-out simulation:
## Probability of down-and-out: 0 percent
## 
## Mean portfolio index value after 20 years: 280.011 kr.
## SD of portfolio index value after 20 years: 123.891 kr.
## Min total portfolio index value after 20 years: 0.355 kr.
## Max total portfolio index value after 20 years: 887.871 kr.
## 
## Share of paths finishing below 100: 4.96 percent

Velliv medium risk, 2007 - 2023

Fit to skew t distribution

## 
## AIC: -34.35752 
## BIC: -31.02467 
## m: 0.05171176 
## s: 0.1149408 
## nu (df): 2.706099 
## xi: 0.5049945 
## R^2: 0.978 
## 
## An R^2 of 0.978 suggests that the fit is very good.
## 
## What is the risk of losing max 10 %? =< 5.4 percent
## What is the risk of losing max 25 %? =< 1.3 percent
## What is the risk of losing max 50 %? =< 0.2 percent
## What is the risk of losing max 90 %? =< 0 percent
## What is the risk of losing max 99 %? =< 0 percent
## 
## What is the chance of gaining min 10 %? >= 36.2 percent
## What is the chance of gaining min 25 %? >= 0.3 percent
## What is the chance of gaining min 50 %? >= 0 percent
## What is the chance of gaining min 90 %? >= 0 percent
## What is the chance of gaining min 99 %? >= 0 percent

QQ Plot

The qq plot looks good. Log returns for Velliv high risk seems to be consistent with a skewed t-distribution.

Data vs fit

Let’s plot the fit and the observed returns together.

Estimated distribution

Now lets look at the CDF of the estimated distribution for each 0.1% increment between 0.5% and 99.5% for the estimated distribution:

We see that for a few observations out of a 1000, the losses are disastrous, while the upside is very dampened. But because the disastrous loss in 2008 was followed by a large profit the following year, we see some increased upside for the top percentiles. Beware: A 1.2 return following a 0.8 return doesn’t take us back where we were before the loss. Path dependency! So if returns more or less average out, but high returns have a tendency to follow high losses, that’s bad!

Monte Carlo

## Down-and-out simulation:
## Probability of down-and-out: 0.01 percent
## 
## Mean portfolio index value after 20 years: 295.944 kr.
## SD of portfolio index value after 20 years: 125.426 kr.
## Min total portfolio index value after 20 years: 0 kr.
## Max total portfolio index value after 20 years: 4224.908 kr.
## 
## Share of paths finishing below 100: 3.12 percent

Velliv high risk, 2011 - 2023

Fit to skew t distribution

## 
## AIC: -21.42488 
## BIC: -19.16508 
## m: 0.06471454 
## s: 0.1499924 
## nu (df): 3.144355 
## xi: 0.002367034 
## R^2: 0.991 
## 
## An R^2 of 0.991 suggests that the fit is extremely good.
## 
## What is the risk of losing max 10 %? =< 8.3 percent
## What is the risk of losing max 25 %? =< 2.5 percent
## What is the risk of losing max 50 %? =< 0.4 percent
## What is the risk of losing max 90 %? =< 0 percent
## What is the risk of losing max 99 %? =< 0 percent
## 
## What is the chance of gaining min 10 %? >= 53.3 percent
## What is the chance of gaining min 25 %? >= 0 percent
## What is the chance of gaining min 50 %? >= 0 percent
## What is the chance of gaining min 90 %? >= 0 percent
## What is the chance of gaining min 99 %? >= 0 percent

QQ Plot

The qq plot looks great. Returns for Velliv medium risk seems to be consistent with a skewed t-distribution.

Data vs fit

Let’s plot the fit and the observed returns together.

Estimated distribution

Now lets look at the CDF of the estimated distribution for each 0.1% increment between 0.5% and 99.5% for the estimated distribution:

We see that for a few observations out of a 1000, the losses are disastrous, while the upside is very dampened.

Monte Carlo

## Down-and-out simulation:
## Probability of down-and-out: 0 percent
## 
## Mean portfolio index value after 20 years: 401.257 kr.
## SD of portfolio index value after 20 years: 217.631 kr.
## Min total portfolio index value after 20 years: 0.129 kr.
## Max total portfolio index value after 20 years: 1694.859 kr.
## 
## Share of paths finishing below 100: 4.36 percent

PFA medium risk, 2011 - 2023

Fit to skew t distribution

## 
## AIC: -33.22998 
## BIC: -30.97018 
## m: 0.05789224 
## s: 0.1234592 
## nu (df): 2.265273 
## xi: 0.477324 
## R^2: 0.991 
## 
## An R^2 of 0.991 suggests that the fit is extremely good.
## 
## What is the risk of losing max 10 %? =< 3.3 percent
## What is the risk of losing max 25 %? =< 0.9 percent
## What is the risk of losing max 50 %? =< 0.2 percent
## What is the risk of losing max 90 %? =< 0 percent
## What is the risk of losing max 99 %? =< 0 percent
## 
## What is the chance of gaining min 10 %? >= 32.7 percent
## What is the chance of gaining min 25 %? >= 0.1 percent
## What is the chance of gaining min 50 %? >= 0 percent
## What is the chance of gaining min 90 %? >= 0 percent
## What is the chance of gaining min 99 %? >= 0 percent

QQ Plot

The qq plot looks great. Log returns for PFA medium risk seems to be consistent with a skewed t-distribution.

##  [1] -0.091256521 -0.003731241  0.027312079  0.045808232  0.059068633
##  [6]  0.069575113  0.078454727  0.086316936  0.093536451  0.100370932
## [11]  0.107018607  0.114081432  0.127604387

Data vs fit

Let’s plot the fit and the observed returns together.

Estimated distribution

Now lets look at the CDF of the estimated distribution for each 0.1% increment between 0.5% and 99.5% for the estimated distribution:

We see that for a few observations out of a 1000, the losses are disastrous. While there is some uptick at the top percentiles, the curve basically flattens out.

Monte Carlo

## Down-and-out simulation:
## Probability of down-and-out: 0 percent
## 
## Mean portfolio index value after 20 years: 325.243 kr.
## SD of portfolio index value after 20 years: 107.181 kr.
## Min total portfolio index value after 20 years: 0.42 kr.
## Max total portfolio index value after 20 years: 1798.741 kr.
## 
## Share of paths finishing below 100: 1.86 percent

PFA high risk, 2011 - 2023

Fit to skew t distribution

## 
## AIC: -23.72565 
## BIC: -21.46585 
## m: 0.08386034 
## s: 0.1210107 
## nu (df): 3.184569 
## xi: 0.01790306 
## R^2: 0.964 
## 
## An R^2 of 0.964 suggests that the fit is very good.
## 
## What is the risk of losing max 10 %? =< 5.3 percent
## What is the risk of losing max 25 %? =< 1.4 percent
## What is the risk of losing max 50 %? =< 0.2 percent
## What is the risk of losing max 90 %? =< 0 percent
## What is the risk of losing max 99 %? =< 0 percent
## 
## What is the chance of gaining min 10 %? >= 59.6 percent
## What is the chance of gaining min 25 %? >= 0 percent
## What is the chance of gaining min 50 %? >= 0 percent
## What is the chance of gaining min 90 %? >= 0 percent
## What is the chance of gaining min 99 %? >= 0 percent

QQ Plot

The qq plot looks ok. Returns for PFA high risk seems to be consistent with a skewed t-distribution.

Data vs fit

Let’s plot the fit and the observed returns together.

Estimated distribution

Now lets look at the CDF of the estimated distribution for each 0.1% increment between 0.5% and 99.5% for the estimated distribution:

We see that for a few observations out of a 1000, the losses are disastrous, while the upside is very dampened.

Monte Carlo

## Down-and-out simulation:
## Probability of down-and-out: 0 percent
## 
## Mean portfolio index value after 20 years: 550.065 kr.
## SD of portfolio index value after 20 years: 240.966 kr.
## Min total portfolio index value after 20 years: 0.071 kr.
## Max total portfolio index value after 20 years: 1691.747 kr.
## 
## Share of paths finishing below 100: 0.94 percent

Mix medium risk, 2011 - 2023

Fit to skew t distribution

## 
## AIC: -36.9603 
## BIC: -34.7005 
## m: 0.05902873 
## s: 0.08757749 
## nu (df): 2.772621 
## xi: 0.02904471 
## R^2: 0.89 
## 
## An R^2 of 0.89 suggests that the fit is not completely random.
## 
## What is the risk of losing max 10 %? =< 3.3 percent
## What is the risk of losing max 25 %? =< 0.7 percent
## What is the risk of losing max 50 %? =< 0.1 percent
## What is the risk of losing max 90 %? =< 0 percent
## What is the risk of losing max 99 %? =< 0 percent
## 
## What is the chance of gaining min 10 %? >= 35.6 percent
## What is the chance of gaining min 25 %? >= 0 percent
## What is the chance of gaining min 50 %? >= 0 percent
## What is the chance of gaining min 90 %? >= 0 percent
## What is the chance of gaining min 99 %? >= 0 percent

QQ Plot

The fit suggests big losses for the lowest percentiles, which are not present in the data.
So the fit is actually a very cautious estimate.

Data vs fit

Let’s plot the fit and the observed returns together.

Interestingly, the fit predicts a much bigger “biggest loss” than the actual data. This is the main reason that R^2 is 0.90 and not higher.

Estimated distribution

Now lets look at the CDF of the estimated distribution for each 0.1% increment between 0.5% and 99.5% for the estimated distribution:

We see that for a few observations out of a 1000, the losses are disastrous, while the upside is very dampened.

Monte Carlo

Version a: Simulation from estimated distribution of returns of mix.

## Down-and-out simulation:
## Probability of down-and-out: 0 percent
## 
## Mean portfolio index value after 20 years: 324.254 kr.
## SD of portfolio index value after 20 years: 97.252 kr.
## Min total portfolio index value after 20 years: 2.698 kr.
## Max total portfolio index value after 20 years: 671.053 kr.
## 
## Share of paths finishing below 100: 0.99 percent

Version b: Mix of simulations from estimated distribution of returns from individual funds.

## Down-and-out simulation:
## Probability of down-and-out: 0 percent
## 
## Mean portfolio index value after 20 years: 301.205 kr.
## SD of portfolio index value after 20 years: 80.503 kr.
## Min total portfolio index value after 20 years: 42.72 kr.
## Max total portfolio index value after 20 years: 927.711 kr.
## 
## Share of paths finishing below 100: 0.24 percent

Mix high risk, 2011 - 2023

Fit to skew t distribution

## 
## AIC: -24.26084 
## BIC: -22.00104 
## m: 0.0822419 
## s: 0.07129843 
## nu (df): 89.86289 
## xi: 0.7697502 
## R^2: 0.961 
## 
## An R^2 of 0.961 suggests that the fit is very good.
## 
## What is the risk of losing max 10 %? =< 0.9 percent
## What is the risk of losing max 25 %? =< 0 percent
## What is the risk of losing max 50 %? =< 0 percent
## What is the risk of losing max 90 %? =< 0 percent
## What is the risk of losing max 99 %? =< 0 percent
## 
## What is the chance of gaining min 10 %? >= 46.1 percent
## What is the chance of gaining min 25 %? >= 1.2 percent
## What is the chance of gaining min 50 %? >= 0 percent
## What is the chance of gaining min 90 %? >= 0 percent
## What is the chance of gaining min 99 %? >= 0 percent

QQ Plot

The qq plot looks good Returns for mixed medium risk portfolios seems to be consistent with a skewed t-distribution.

Data vs fit

Let’s plot the fit and the observed returns together.

Estimated distribution

Now lets look at the CDF of the estimated distribution for each 0.1% increment between 0.5% and 99.5% for the estimated distribution:

We see that the high risk mix provides a much better upside and smaller downside.

Monte Carlo

Version a: Simulation from estimated distribution of returns of mix.

## Down-and-out simulation:
## Probability of down-and-out: 0 percent
## 
## Mean portfolio index value after 20 years: 502.823 kr.
## SD of portfolio index value after 20 years: 156.506 kr.
## Min total portfolio index value after 20 years: 153.914 kr.
## Max total portfolio index value after 20 years: 1399.603 kr.
## 
## Share of paths finishing below 100: 0 percent

Version b: Mix of simulations from estimated distribution of returns from individual funds.

## Down-and-out simulation:
## Probability of down-and-out: 0 percent
## 
## Mean portfolio index value after 20 years: 479.657 kr.
## SD of portfolio index value after 20 years: 165.401 kr.
## Min total portfolio index value after 20 years: 41.683 kr.
## Max total portfolio index value after 20 years: 1263.094 kr.
## 
## Share of paths finishing below 100: 0.14 percent

Compare pension plans

Risk of max loss

Risk of max loss of x percent for a single period (year).
x values are row names.

Velliv_medium Velliv_medium_long Velliv_high PFA_medium PFA_high mix_medium mix_high
0 21.3 18.2 19.9 12.2 14.3 12.7 13.0
5 12.5 9.6 12.8 6.0 8.6 6.2 4.2
10 7.4 5.4 8.3 3.3 5.3 3.3 0.9
25 1.8 1.3 2.5 0.9 1.4 0.7 0.0
50 0.2 0.2 0.4 0.2 0.2 0.1 0.0
90 0.0 0.0 0.0 0.0 0.0 0.0 0.0
99 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Worst ranking for loss percentiles

0 ranking 5 ranking 10 ranking 25 ranking 50 ranking 90 ranking 99 ranking
21.3 Velliv_medium 12.8 Velliv_high 8.3 Velliv_high 2.5 Velliv_high 0.4 Velliv_high 0 Velliv_medium 0 Velliv_medium
19.9 Velliv_high 12.5 Velliv_medium 7.4 Velliv_medium 1.8 Velliv_medium 0.2 Velliv_medium 0 Velliv_medium_long 0 Velliv_medium_long
18.2 Velliv_medium_long 9.6 Velliv_medium_long 5.4 Velliv_medium_long 1.4 PFA_high 0.2 Velliv_medium_long 0 Velliv_high 0 Velliv_high
14.3 PFA_high 8.6 PFA_high 5.3 PFA_high 1.3 Velliv_medium_long 0.2 PFA_medium 0 PFA_medium 0 PFA_medium
13.0 mix_high 6.2 mix_medium 3.3 PFA_medium 0.9 PFA_medium 0.2 PFA_high 0 PFA_high 0 PFA_high
12.7 mix_medium 6.0 PFA_medium 3.3 mix_medium 0.7 mix_medium 0.1 mix_medium 0 mix_medium 0 mix_medium
12.2 PFA_medium 4.2 mix_high 0.9 mix_high 0.0 mix_high 0.0 mix_high 0 mix_high 0 mix_high

Chance of min gains

Chance of min gains of x percent for a single period (year).
x values are row names.

Velliv_medium Velliv_medium_long Velliv_high PFA_medium PFA_high mix_medium mix_high
0 78.7 81.8 80.1 87.8 85.7 87.3 87.0
5 63.8 64.9 69.2 71.5 75.8 71.4 69.9
10 41.0 36.2 53.3 32.7 59.6 35.6 46.1
25 0.0 0.3 0.0 0.1 0.0 0.0 1.2
50 0.0 0.0 0.0 0.0 0.0 0.0 0.0
100 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Best ranking for gains percentiles

0 ranking 5 ranking 10 ranking 25 ranking 50 ranking 100 ranking
87.8 PFA_medium 75.8 PFA_high 59.6 PFA_high 1.2 mix_high 0 Velliv_medium 0 Velliv_medium
87.3 mix_medium 71.5 PFA_medium 53.3 Velliv_high 0.3 Velliv_medium_long 0 Velliv_medium_long 0 Velliv_medium_long
87.0 mix_high 71.4 mix_medium 46.1 mix_high 0.1 PFA_medium 0 Velliv_high 0 Velliv_high
85.7 PFA_high 69.9 mix_high 41.0 Velliv_medium 0.0 Velliv_medium 0 PFA_medium 0 PFA_medium
81.8 Velliv_medium_long 69.2 Velliv_high 36.2 Velliv_medium_long 0.0 Velliv_high 0 PFA_high 0 PFA_high
80.1 Velliv_high 64.9 Velliv_medium_long 35.6 mix_medium 0.0 PFA_high 0 mix_medium 0 mix_medium
78.7 Velliv_medium 63.8 Velliv_medium 32.7 PFA_medium 0.0 mix_medium 0 mix_high 0 mix_high

MC risk percentiles

Risk of loss from first to last period.

_a is simulation from estimated distribution of returns of mix.
_b is mix of simulations from estimated distribution of returns from individual funds.

_m is medium.
_h is high.

Velliv_m Velliv_m_long Velliv_h PFA_m PFA_h mix_m_a mix_h_a mix_m_b mix_h_b
0 4.96 3.12 4.36 1.86 0.94 0.99 0 0.24 0.14
5 4.25 2.65 3.92 1.65 0.83 0.80 0 0.20 0.12
10 3.76 2.46 3.51 1.48 0.72 0.75 0 0.17 0.11
25 2.38 1.68 2.43 1.06 0.52 0.45 0 0.12 0.04
50 0.81 0.71 1.14 0.49 0.21 0.21 0 0.02 0.01
90 0.05 0.09 0.07 0.09 0.06 0.04 0 0.00 0.00
99 0.02 0.02 0.01 0.02 0.02 0.00 0 0.00 0.00

Worst ranking for MC loss percentiles

0 ranking 5 ranking 10 ranking 25 ranking 50 ranking 90 ranking 99 ranking
4.96 Velliv_m 4.25 Velliv_m 3.76 Velliv_m 2.43 Velliv_h 1.14 Velliv_h 0.09 Velliv_m_long 0.02 Velliv_m
4.36 Velliv_h 3.92 Velliv_h 3.51 Velliv_h 2.38 Velliv_m 0.81 Velliv_m 0.09 PFA_m 0.02 Velliv_m_long
3.12 Velliv_m_long 2.65 Velliv_m_long 2.46 Velliv_m_long 1.68 Velliv_m_long 0.71 Velliv_m_long 0.07 Velliv_h 0.02 PFA_m
1.86 PFA_m 1.65 PFA_m 1.48 PFA_m 1.06 PFA_m 0.49 PFA_m 0.06 PFA_h 0.02 PFA_h
0.99 mix_m_a 0.83 PFA_h 0.75 mix_m_a 0.52 PFA_h 0.21 PFA_h 0.05 Velliv_m 0.01 Velliv_h
0.94 PFA_h 0.80 mix_m_a 0.72 PFA_h 0.45 mix_m_a 0.21 mix_m_a 0.04 mix_m_a 0.00 mix_m_a
0.24 mix_m_b 0.20 mix_m_b 0.17 mix_m_b 0.12 mix_m_b 0.02 mix_m_b 0.00 mix_h_a 0.00 mix_h_a
0.14 mix_h_b 0.12 mix_h_b 0.11 mix_h_b 0.04 mix_h_b 0.01 mix_h_b 0.00 mix_m_b 0.00 mix_m_b
0.00 mix_h_a 0.00 mix_h_a 0.00 mix_h_a 0.00 mix_h_a 0.00 mix_h_a 0.00 mix_h_b 0.00 mix_h_b

MC gains percentiles

Chance of gains from first to last period.
_a is simulation from estimated distribution of returns of mix.
_b is mix of simulations from estimated distribution of returns from individual funds.

Velliv_m Velliv_m_long Velliv_h PFA_m PFA_h mix_m_a mix_h_a mix_m_b mix_h_b
0 95.04 96.88 95.64 98.14 99.06 99.01 100.00 99.76 99.86
5 94.36 96.38 95.19 97.87 98.96 98.88 100.00 99.67 99.82
10 93.71 95.82 94.80 97.69 98.87 98.70 100.00 99.60 99.80
25 91.04 94.19 93.10 96.82 98.49 98.08 100.00 99.17 99.68
50 85.69 90.00 90.03 94.98 97.53 96.49 100.00 97.87 99.26
100 71.71 79.03 82.08 88.39 94.65 89.62 99.72 89.85 97.48
200 39.81 45.49 63.48 59.71 85.23 59.79 93.29 48.81 86.91
300 17.14 18.12 44.16 23.58 70.99 21.65 72.69 11.10 65.58
400 5.25 4.92 28.13 4.68 54.38 3.60 45.45 1.04 41.81
500 1.30 1.21 17.36 0.60 38.28 0.26 23.48 0.06 22.45
1000 0.00 0.03 0.52 0.02 2.30 0.00 0.31 0.00 0.10

Best ranking for MC gains percentiles

0 ranking 5 ranking 10 ranking 25 ranking 50 ranking 100 ranking 200 ranking 300 ranking 400 ranking 500 ranking 1000 ranking
100.00 mix_h_a 100.00 mix_h_a 100.00 mix_h_a 100.00 mix_h_a 100.00 mix_h_a 99.72 mix_h_a 93.29 mix_h_a 72.69 mix_h_a 54.38 PFA_h 38.28 PFA_h 2.30 PFA_h
99.86 mix_h_b 99.82 mix_h_b 99.80 mix_h_b 99.68 mix_h_b 99.26 mix_h_b 97.48 mix_h_b 86.91 mix_h_b 70.99 PFA_h 45.45 mix_h_a 23.48 mix_h_a 0.52 Velliv_h
99.76 mix_m_b 99.67 mix_m_b 99.60 mix_m_b 99.17 mix_m_b 97.87 mix_m_b 94.65 PFA_h 85.23 PFA_h 65.58 mix_h_b 41.81 mix_h_b 22.45 mix_h_b 0.31 mix_h_a
99.06 PFA_h 98.96 PFA_h 98.87 PFA_h 98.49 PFA_h 97.53 PFA_h 89.85 mix_m_b 63.48 Velliv_h 44.16 Velliv_h 28.13 Velliv_h 17.36 Velliv_h 0.10 mix_h_b
99.01 mix_m_a 98.88 mix_m_a 98.70 mix_m_a 98.08 mix_m_a 96.49 mix_m_a 89.62 mix_m_a 59.79 mix_m_a 23.58 PFA_m 5.25 Velliv_m 1.30 Velliv_m 0.03 Velliv_m_long
98.14 PFA_m 97.87 PFA_m 97.69 PFA_m 96.82 PFA_m 94.98 PFA_m 88.39 PFA_m 59.71 PFA_m 21.65 mix_m_a 4.92 Velliv_m_long 1.21 Velliv_m_long 0.02 PFA_m
96.88 Velliv_m_long 96.38 Velliv_m_long 95.82 Velliv_m_long 94.19 Velliv_m_long 90.03 Velliv_h 82.08 Velliv_h 48.81 mix_m_b 18.12 Velliv_m_long 4.68 PFA_m 0.60 PFA_m 0.00 Velliv_m
95.64 Velliv_h 95.19 Velliv_h 94.80 Velliv_h 93.10 Velliv_h 90.00 Velliv_m_long 79.03 Velliv_m_long 45.49 Velliv_m_long 17.14 Velliv_m 3.60 mix_m_a 0.26 mix_m_a 0.00 mix_m_a
95.04 Velliv_m 94.36 Velliv_m 93.71 Velliv_m 91.04 Velliv_m 85.69 Velliv_m 71.71 Velliv_m 39.81 Velliv_m 11.10 mix_m_b 1.04 mix_m_b 0.06 mix_m_b 0.00 mix_m_b

Summary statistics

Fit summary

Summary for fit of log returns to an F-S skew standardized Student-t distribution.
m is the location parameter.
s is the scale parameter.
nu is the estimated degrees of freedom, or shape parameter.
xi is the estimated skewness parameter.

Velliv_medium Velliv_medium_long Velliv_high PFA_medium PFA_high mix_medium mix_high
m 0.048 0.052 0.065 0.058 0.084 0.059 0.082
s 0.120 0.115 0.150 0.123 0.121 0.088 0.071
nu 3.304 2.706 3.144 2.265 3.185 2.773 89.863
xi 0.034 0.505 0.002 0.477 0.018 0.029 0.770
R-squared 0.993 0.978 0.991 0.991 0.964 0.890 0.961

Fit statistics ranking

m ranking s ranking R-squared ranking
0.084 PFA_high 0.071 mix_high 0.993 Velliv_medium
0.082 mix_high 0.088 mix_medium 0.991 Velliv_high
0.065 Velliv_high 0.115 Velliv_medium_long 0.991 PFA_medium
0.059 mix_medium 0.120 Velliv_medium 0.978 Velliv_medium_long
0.058 PFA_medium 0.121 PFA_high 0.964 PFA_high
0.052 Velliv_medium_long 0.123 PFA_medium 0.961 mix_high
0.048 Velliv_medium 0.150 Velliv_high 0.890 mix_medium

Monte Carlo simulations summary

Monte Carlo simulations of portfolio index values (currency values).
Statistics are given for the final state of all paths.
Probability of down-and_out is calculated as the share of paths that reach 0 at some point. All subsequent values for a path are set to 0, if the path reaches at any point.
0 is defined as any value below a threshold.
losing_prob_pct is the probability of losing money. This is calculated as the share of paths finishing below index 100.

## Number of paths: 10000
Velliv_m Velliv_m_l Velliv_h PFA_m PFA_h mix_m_a mix_m_b mix_h_a mix_h_b
mc_m 280.011 295.944 401.257 325.243 550.065 324.254 301.205 502.823 479.657
mc_s 123.891 125.426 217.631 107.181 240.966 97.252 80.503 156.506 165.401
mc_min 0.355 0.000 0.129 0.420 0.071 2.698 42.720 153.914 41.683
mc_max 887.871 4224.908 1694.859 1798.741 1691.747 671.053 927.711 1399.603 1263.094
dao_pct 0.000 0.010 0.000 0.000 0.000 0.000 0.000 0.000 0.000
losing_pct 4.960 3.120 4.360 1.860 0.940 0.990 0.240 0.000 0.140

Ranking

mc_m ranking mc_s ranking mc_min ranking mc_max ranking dao_pct ranking losing_pct ranking
550.065 PFA_h 80.503 mix_m_b 153.914 mix_h_a 4224.908 Velliv_m_l 0.00 Velliv_m 0.00 mix_h_a
502.823 mix_h_a 97.252 mix_m_a 42.720 mix_m_b 1798.741 PFA_m 0.00 Velliv_h 0.14 mix_h_b
479.657 mix_h_b 107.181 PFA_m 41.683 mix_h_b 1694.859 Velliv_h 0.00 PFA_m 0.24 mix_m_b
401.257 Velliv_h 123.891 Velliv_m 2.698 mix_m_a 1691.747 PFA_h 0.00 PFA_h 0.94 PFA_h
325.243 PFA_m 125.426 Velliv_m_l 0.420 PFA_m 1399.603 mix_h_a 0.00 mix_m_a 0.99 mix_m_a
324.254 mix_m_a 156.506 mix_h_a 0.355 Velliv_m 1263.094 mix_h_b 0.00 mix_m_b 1.86 PFA_m
301.205 mix_m_b 165.401 mix_h_b 0.129 Velliv_h 927.711 mix_m_b 0.00 mix_h_a 3.12 Velliv_m_l
295.944 Velliv_m_l 217.631 Velliv_h 0.071 PFA_h 887.871 Velliv_m 0.00 mix_h_b 4.36 Velliv_h
280.011 Velliv_m 240.966 PFA_h 0.000 Velliv_m_l 671.053 mix_m_a 0.01 Velliv_m_l 4.96 Velliv_m

Appendix

Average of returns vs returns of average

Math

\[\text{Avg. of returns} := \dfrac{ \left(\dfrac{x_t}{x_{t-1}} + \dfrac{y_t}{y_{t-1}} \right) }{2}\] \[\text{Returns of avg.} := \left(\dfrac{ x_t + y_t }{2}\right) \Big/ \left(\dfrac{ x_{t-1} + y_{t-1} }{2}\right) \equiv \dfrac{ x_t + y_t }{ x_{t-1} + y_{t-1}}\]

For which \(x_1\) and \(y_1\) are \(\text{Avg. of returns} = \text{Returns of avg.}\)?

\[\dfrac{ \left(\dfrac{x_t}{x_{t-1}} + \dfrac{y_t}{y_{t-1}} \right) }{2} = \dfrac{ x_t + y_t }{ x_{t-1} + y_{t-1}}\]

\[\dfrac{x_t}{x_{t-1}} + \dfrac{y_t}{y_{t-1}} = 2 \dfrac{ x_t + y_t }{ x_{t-1} + y_{t-1}}\]

c{ x_t + y_t }{ x_{t-1} + y_{t-1}} \[ \](x_{t-1} + y_{t-1}) x_t y_{t-1} + (x_{t-1} + y_{t-1}) x_{t-1} y_t = 2 (x_{t-1}y_{t-1}x_t + x_{t-1}y_{t-1}y_t)$$

\[(x_{t-1}x_1y_{t-1} + y_{t-1}x_ty_{t-1}) + (x_{t-1}x_{t-1}y_t + x_{t-1}y_{t-1}y_t) = 2(x_{t-1}y_{t-1}x_t + x_{t-1}y_{t-1}y_t)\] This is not generally true, but true if for instance \(x_{t-1} = y_{t-1}\).

Example

Definition: R = 1+r

## Let x_0 be 100.
## Let y_0 be 200.
## So the initial value of the pf is 300 .
## Let R_x be 0.5.
## Let R_y be 1.5.

Then,

## x_1 is R_x * x_0 = 50.
## y_1 is R_y * y_0 = 300.

Average of returns:

## 0.5 * (R_x + R_y) = 1

So here the value of the pf at t=1 should be unchanged from t=0:

## (x_0 + y_0) * 0.5 * (R_x + R_y) = 300

But this is clearly not the case:

## 0.5 * (x_1 + y_1) = 0.5 * (R_x * x_0 + R_y * y_0) = 175

Therefore we should take returns of average, not average of returns!

Let’s take the average of log returns instead:

## 0.5 * (log(R_x) + log(R_y)) = -0.143841

We now get:

## (x_0 + y_0) * exp(0.5 * (log(Rx) + log(Ry))) = 259.8076

So taking the average of log returns doesn’t work either.

Simulation of mix vs mix of simulations

Test if a simulation of a mix (average) of two returns series has the same distribution as a mix of two simulated returns series.

## m(data_x): -0.001097715 
## s(data_x): 0.2821873 
## m(data_y): 9.637536 
## s(data_y): 2.753657 
## 
## m(data_x + data_y): 4.818219 
## s(data_x + data_y): 1.378236

m and s of final state of all paths.
_a is mix of simulated returns.
_b is simulated mixed returns.

m_a m_b s_a s_b
96.461 96.123 6.145 6.358
96.692 96.035 6.237 6.153
96.607 96.119 6.229 6.232
96.305 96.411 6.148 6.111
96.242 96.740 6.635 6.292
96.491 96.459 6.145 6.006
96.300 96.257 6.279 6.223
96.314 96.681 6.285 5.996
96.192 96.162 6.277 6.359
96.046 96.387 6.280 6.179 
##       m_a             m_b             s_a             s_b       
##  Min.   :96.05   Min.   :96.04   Min.   :6.145   Min.   :5.996  
##  1st Qu.:96.26   1st Qu.:96.13   1st Qu.:6.168   1st Qu.:6.122  
##  Median :96.31   Median :96.32   Median :6.257   Median :6.201  
##  Mean   :96.37   Mean   :96.34   Mean   :6.266   Mean   :6.191  
##  3rd Qu.:96.48   3rd Qu.:96.45   3rd Qu.:6.280   3rd Qu.:6.277  
##  Max.   :96.69   Max.   :96.74   Max.   :6.635   Max.   :6.359

_a and _b are very close to equal.
We attribute the differences to differences in estimating the distributions in version a and b.

The final state is independent of the order of the preceding steps:

So does the order of the steps in the two processes matter, when mixing simulated returns?

The order of steps in the individual paths do not matter, because the mix of simulated paths is a sum of a sum, so the order of terms doesn’t affect the sum. If there is variation it is because the sets preceding steps are not the same. For instance, the steps between step 1 and 60 in the plot above are not the same for the two lines.

Recall, \[\text{Var}(aX+bY) = a^2 \text{Var}(X) + b^2 \text{Var}(Y) + 2ab \text{Cov}(a, b)\]

var(0.5 * vhr + 0.5 * phr)
## [1] 0.005355618
0.5^2 * var(vhr) + 0.5^2 * var(phr) + 2 * 0.5 * 0.5 * cov(vhr, phr)
## [1] 0.005355618

Our distribution estimate is based on 13 observations. Is that enough for a robust estimate? What if we suddenly hit a year like 2008? How would that affect our estimate?
Let’s try to include the Velliv data from 2007-2010.
We do this by sampling 13 observations from vmrl.

##        m                 s          
##  Min.   :0.06244   Min.   :0.05177  
##  1st Qu.:0.06673   1st Qu.:0.06175  
##  Median :0.06991   Median :0.06701  
##  Mean   :0.07089   Mean   :0.06861  
##  3rd Qu.:0.07370   3rd Qu.:0.07416  
##  Max.   :0.08449   Max.   :0.09370